PyTorch深度学习(五)【卷积神经网络】

卷积神经网络(基础篇):

下采样(Subsampling):通道数不变,减少数据量,降低运算需求。

做这个卷积:

网络:

最大池化层(MaxPooling):通道数不变,图像大小缩成原来的一半,没有权重。

代码:

复制代码
import torchfrom torchvision import transformsfrom torchvision import datasetsfrom torch.utils.data import DataLoaderimport torch.nn.functional as Fimport torch.optim as optim​# prepare dataset​batch_size = 64transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])​train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform)train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform)test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)​# design model using class​class Net(torch.nn.Module):    def __init__(self):        super(Net, self).__init__()        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)        self.pooling = torch.nn.MaxPool2d(2)        self.fc = torch.nn.Linear(320, 10)​    def forward(self, x):        # flatten data from (n,1,28,28) to (n, 784)        batch_size = x.size(0)     #求batchsize        x = F.relu(self.pooling(self.conv1(x)))   #卷积、池化、激活        x = F.relu(self.pooling(self.conv2(x)))        x = x.view(batch_size, -1)     # -1 此处自动算出的是320;view的目的就是变成全连接网络需要的格式。flatten        x = self.fc(x)​        return x​model = Net()​device = torch.device("cuda" if torch.cuda.is_available() else "cpu")    #如果你有GPU,这两行的意思就是用GPU跑model.to(device)   #没有GPU的话这两行可以不写(写上也无妨)​# construct loss and optimizercriterion = torch.nn.CrossEntropyLoss()optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)​# training cycle forward, backward, update​def train(epoch):    running_loss = 0.0    for batch_idx, data in enumerate(train_loader, 0):        inputs, target = data        inputs, target = inputs.to(device), target.to(device)#把数据迁移到GPU上,如果没有GPU,这行可以不写        optimizer.zero_grad()​        outputs = model(inputs)        loss = criterion(outputs, target)        loss.backward()        optimizer.step()​        running_loss += loss.item()        if batch_idx % 300 == 299:            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))            running_loss = 0.0​def test():    correct = 0    total = 0    with torch.no_grad():        for data in test_loader:            images, labels = data            images, labels = images.to(device), labels.to(device)   #如果没有GPU,这行可以不写            outputs = model(images)            _, predicted = torch.max(outputs.data, dim=1)            total += labels.size(0)            correct += (predicted == labels).sum().item()    print('accuracy on test set: %d %% ' % (100 * correct / total))​if __name__ == '__main__':    for epoch in range(10):        train(epoch)        test()

经过10轮的训练后,模型的准确度达到了98%,用线性模型是97%。

卷积神经网络(提高篇):

Inception Module:

Concatenate:把张量拼接到一块;

Average Pooling:均值池化;

1×1卷积:将来的卷积核是1×1,个数取决于输出张量的通道。

1×1卷积作用:(主要就是降维)

部分模块代码:

代码:

复制代码
import torchimport torch.nn as nnfrom torchvision import transformsfrom torchvision import datasetsfrom torch.utils.data import DataLoaderimport torch.nn.functional as Fimport torch.optim as optim​# prepare dataset​batch_size = 64transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])  # 归一化,均值和方差​train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform)train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform)test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)​# design model using classclass InceptionA(nn.Module):    def __init__(self, in_channels):        super(InceptionA, self).__init__()        self.branch1x1 = nn.Conv2d(in_channels, 16, kernel_size=1)​        self.branch5x5_1 = nn.Conv2d(in_channels, 16, kernel_size=1)        self.branch5x5_2 = nn.Conv2d(16, 24, kernel_size=5, padding=2)​        self.branch3x3_1 = nn.Conv2d(in_channels, 16, kernel_size=1)        self.branch3x3_2 = nn.Conv2d(16, 24, kernel_size=3, padding=1)        self.branch3x3_3 = nn.Conv2d(24, 24, kernel_size=3, padding=1)​        self.branch_pool = nn.Conv2d(in_channels, 24, kernel_size=1)​    def forward(self, x):        branch1x1 = self.branch1x1(x)​        branch5x5 = self.branch5x5_1(x)        branch5x5 = self.branch5x5_2(branch5x5)​        branch3x3 = self.branch3x3_1(x)        branch3x3 = self.branch3x3_2(branch3x3)        branch3x3 = self.branch3x3_3(branch3x3)​        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)        branch_pool = self.branch_pool(branch_pool)​        outputs = [branch1x1, branch5x5, branch3x3, branch_pool]        return torch.cat(outputs, dim=1)  # b,c,w,h  c对应的是dim=1​class Net(nn.Module):    def __init__(self):        super(Net, self).__init__()        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)        self.conv2 = nn.Conv2d(88, 20, kernel_size=5)  # 88 = 24x3 + 16​        self.incep1 = InceptionA(in_channels=10)  # 与conv1 中的10对应        self.incep2 = InceptionA(in_channels=20)  # 与conv2 中的20对应​        self.mp = nn.MaxPool2d(2)        self.fc = nn.Linear(1408, 10)​    def forward(self, x):        in_size = x.size(0)        x = F.relu(self.mp(self.conv1(x)))  #通道变为10        x = self.incep1(x)                  #88        x = F.relu(self.mp(self.conv2(x)))  #20        x = self.incep2(x)                  #88        x = x.view(in_size, -1)        x = self.fc(x)​        return x​model = Net()​# construct loss and optimizercriterion = torch.nn.CrossEntropyLoss()optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)​# training cycle forward, backward, update​def train(epoch):    running_loss = 0.0    for batch_idx, data in enumerate(train_loader, 0):        inputs, target = data        optimizer.zero_grad()​        outputs = model(inputs)        loss = criterion(outputs, target)        loss.backward()        optimizer.step()​        running_loss += loss.item()        if batch_idx % 300 == 299:            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))            running_loss = 0.0​def test():    correct = 0    total = 0    with torch.no_grad():        for data in test_loader:            images, labels = data            outputs = model(images)            _, predicted = torch.max(outputs.data, dim=1)            total += labels.size(0)            correct += (predicted == labels).sum().item()    print('accuracy on test set: %d %% ' % (100 * correct / total))​if __name__ == '__main__':    for epoch in range(10):        train(epoch)        test()

Residual Network:

?:保持输入和输出大小相同。

代码:

复制代码
import torchimport torch.nn as nnfrom torchvision import transformsfrom torchvision import datasetsfrom torch.utils.data import DataLoaderimport torch.nn.functional as Fimport torch.optim as optim​# prepare dataset​batch_size = 64transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])  # 归一化,均值和方差​train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform)train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform)test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)​# design model using classclass ResidualBlock(nn.Module):    def __init__(self, channels):        super(ResidualBlock, self).__init__()        self.channels = channels        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)​    def forward(self, x):        y = F.relu(self.conv1(x))        y = self.conv2(y)        return F.relu(x + y)​class Net(nn.Module):    def __init__(self):        super(Net, self).__init__()        self.conv1 = nn.Conv2d(1, 16, kernel_size=5)        self.conv2 = nn.Conv2d(16, 32, kernel_size=5)  # 88 = 24x3 + 16​        self.rblock1 = ResidualBlock(16)        self.rblock2 = ResidualBlock(32)​        self.mp = nn.MaxPool2d(2)        self.fc = nn.Linear(512, 10)  # 暂时不知道1408咋能自动出来的​    def forward(self, x):        in_size = x.size(0)​        x = self.mp(F.relu(self.conv1(x)))        x = self.rblock1(x)        x = self.mp(F.relu(self.conv2(x)))        x = self.rblock2(x)​        x = x.view(in_size, -1)        x = self.fc(x)        return x​model = Net()​# construct loss and optimizercriterion = torch.nn.CrossEntropyLoss()optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)​# training cycle forward, backward, update​def train(epoch):    running_loss = 0.0    for batch_idx, data in enumerate(train_loader, 0):        inputs, target = data        optimizer.zero_grad()​        outputs = model(inputs)        loss = criterion(outputs, target)        loss.backward()        optimizer.step()​        running_loss += loss.item()        if batch_idx % 300 == 299:            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))            running_loss = 0.0​def test():    correct = 0    total = 0    with torch.no_grad():        for data in test_loader:            images, labels = data            outputs = model(images)            _, predicted = torch.max(outputs.data, dim=1)            total += labels.size(0)            correct += (predicted == labels).sum().item()    print('accuracy on test set: %d %% ' % (100 * correct / total))​if __name__ == '__main__':    for epoch in range(10):        train(epoch)        test()

【番外:1.理论学习 2.阅读文献 3.复现经典 4.扩充视野】

相关推荐
linjoe9914 小时前
【Deep Learning】Ubuntu配置深度学习环境
人工智能·深度学习·ubuntu
深耕AI17 小时前
【PyTorch训练】准确率计算(代码片段拆解)
人工智能·pytorch·python
Blossom.11818 小时前
从“能写”到“能干活”:大模型工具调用(Function-Calling)的工程化落地指南
数据库·人工智能·python·深度学习·机器学习·计算机视觉·oracle
byzy18 小时前
【论文笔记】RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar
论文阅读·深度学习·自动驾驶
飞机火车巴雷特18 小时前
【论文阅读】Uncertainty Modeling for Out-of-Distribution Generalization (ICLR 2022)
论文阅读·深度学习·不确定性建模
lifallen19 小时前
淘宝RecGPT:通过LLM增强推荐
人工智能·深度学习·ai·推荐算法
kunwen12319 小时前
机器学习、深度学习
rnn·langchain·cnn·transformer·langgraph
IT学长编程19 小时前
计算机毕业设计 基于深度学习的酒店评论文本情感分析研究 Python毕业设计项目 Hadoop毕业设计选题 机器学习选题【附源码+文档报告+安装调试】
hadoop·python·深度学习·机器学习·数据分析·毕业设计·酒店评论文本情感分析
nuczzz20 小时前
pytorch非线性回归
人工智能·pytorch·机器学习·ai